TY - GEN
T1 - Spatio-temporal analysis of brain MRI images using hidden Markov models
AU - Wang, Ying
AU - Resnick, Susan M.
AU - Davatzikos, Christos
PY - 2010
Y1 - 2010
N2 - A rapidly increasing number of medical imaging studies is longitudinal, i.e. involves series of repeated examinations of the same individuals. This paper presents a methodology for analysis of such 4D images, with brain aging as the primary application. An adaptive regional clustering method is first adopted to construct a spatial pattern, in which a measure of correlation between morphological measurements and a continuous patient's variable (age in our case) is used to group brain voxels into regions; Secondly, a dynamic probabilistic Hidden Markov Model (HMM) is created to statistically analyze the relationship between spatial brain patterns and hidden states; Thirdly, parametric HMM models under a bagging framework are used to capture the changes occurring with time by decoding the hidden states longitudinally. We apply this method to datasets from elderly individuals, and test the effectiveness of this spatio-temporal model in analyzing the temporal dynamics of spatial aging patterns on an individual basis. Experimental results show this method could facilitate the early detection of pathological brain change.
AB - A rapidly increasing number of medical imaging studies is longitudinal, i.e. involves series of repeated examinations of the same individuals. This paper presents a methodology for analysis of such 4D images, with brain aging as the primary application. An adaptive regional clustering method is first adopted to construct a spatial pattern, in which a measure of correlation between morphological measurements and a continuous patient's variable (age in our case) is used to group brain voxels into regions; Secondly, a dynamic probabilistic Hidden Markov Model (HMM) is created to statistically analyze the relationship between spatial brain patterns and hidden states; Thirdly, parametric HMM models under a bagging framework are used to capture the changes occurring with time by decoding the hidden states longitudinally. We apply this method to datasets from elderly individuals, and test the effectiveness of this spatio-temporal model in analyzing the temporal dynamics of spatial aging patterns on an individual basis. Experimental results show this method could facilitate the early detection of pathological brain change.
UR - http://www.scopus.com/inward/record.url?scp=79960098823&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15745-5_20
DO - 10.1007/978-3-642-15745-5_20
M3 - Conference proceeding contribution
C2 - 20879311
AN - SCOPUS:79960098823
SN - 9783642157448
VL - 6362 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 160
EP - 168
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
PB - Springer, Springer Nature
CY - Beijing, China
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 24 September 2010
ER -